Lifeboat Foundation

To improve access to large data sets, scientists are looking to cloud-based solutions for data management.

In the coming decade, big projects like the Large Hadron Collider (LHC) and the Square Kilometre Array (SKA) are each expected to produce an exabyte of data yearly, which is about 20 times the digital content of all the written works throughout human history. This information overload requires new thinking about data management, which is why scientists have begun to look to the “cloud.” In such a scenario, data would be stored and analyzed remotely, with the advantage that information would become more accessible to a wider scientific community. Efforts are underway to create “science clouds,” but disagreements remain over their structure and implementation. To discuss these details, around 60 scientists came together for The Science Cloud meeting in Bad Honnef, Germany. The attendees shared lessons from past and ongoing projects in the hope of building the groundwork for a future scientific computing infrastructure.

How molecules change when they react to stimuli such as light is fundamental in biology, for example during photosynthesis. Scientists have been working to unravel the workings of these changes in several fields, and by combining two of these, researchers have paved the way for a new era in understanding the reactions of protein molecules fundamental for life.

The large international research team, led by Professor Jasper van Thor from the Department of Life Sciences at Imperial, report their results in the journal Nature Chemistry.

Crystallography is a powerful technique in structural biology for taking ‘snapshots’ of how molecules are arranged. Over several large-scale experiments and years of theory work, the team behind the new study integrated this with another technique that maps vibrations in the electronic and nuclear configuration of molecules, called spectroscopy.

Commercial sensors will be reliable, tiny, and affordable.

(Suggested reading: “The Non-things”, Byung-Chul Han).

The idea that human connection will be crucial in the age of superintelligence is not uncommon among some experts and futurists. Superintelligence refers to a hypothetical form of artificial intelligence that surpasses human intelligence across all domains. Some individuals, including scientists and technologists, have expressed concerns about the potential impact of superintelligence on humanity.

Some argument that if superintelligence were to become a reality, it could bring about profound changes to society and the way we live. In such a scenario, human traits and values like compassion, empathy, and emotional connections could become even more critical to preserve our uniqueness and counterbalance the overwhelming capabilities of superintelligent machines. The idea is that our ability to connect with each other emotionally and maintain a sense of community, compassion, and mutual understanding could provide a counterpoint to the cold and logical calculations of superintelligent AI. This human element might serve as a safeguard against potential negative consequences or misuse of superintelligence.

Smartphones and other tech pose special challenges — and opportunities — for young brains.

Stem cells are special kinds of cells in our body that can become any other type of cell. They have huge potential for medicine, and trials are currently under way using stem cells to replace damaged cells in diseases like Parkinson’s.

One way to get stem cells is from human embryos, but this has ethical concerns and practical limitations. Another way is to turn adult cells from the skin or elsewhere into what are called “induced pluripotent stem cells” (iPS cells).

However, these cells sometimes carry a “memory” of the kind of cell they used to be, which can make them less predictable or efficient when we try to turn them into other types of cells.

In a new AI research, a team of MIT and Harvard University researchers has introduced a groundbreaking framework called “Follow Anything” (FAn). The system addresses the limitations of current object-following robotic systems and presents an innovative solution for real-time, open-set object tracking and following.

The primary shortcomings of existing robotic object-following systems are a constrained ability to accommodate new objects due to a fixed set of recognized categories and a lack of user-friendliness in specifying target objects. The new FAn system tackles these issues by presenting an open-set approach that can seamlessly detect, segment, track, and follow a wide range of things while adapting to novel objects through text, images, or click queries.

The core features of the proposed FAn system can be summarized as follows:

The development of robotic avatars could benefit from an improvement in how computers detect objects in low-resolution images.

A team at RIKEN has improved computer vision recognition capabilities by training algorithms to better identify objects in low-resolution images. Inspired by human brain memory formation techniques, the model degrades the quality of high-resolution images to train the algorithm in self-supervised learning, enhancing object recognition in low-quality images. The development is expected to benefit not only traditional computer vision applications but also the creation of cybernetic avatars and terahertz imaging technology.

Robotic avatar vision enhancement inspired by human perception.

An “out there” theory inspired the development of the Dark Matter Radio, a device that could explain the mysterious matter that makes up 85 percent of the mass of our universe.